Cluster Analysis of Ranunculus Species

The aim of the experiment was to examine whether the morphological characters of eleven species of Ranunculus collected from a number of populations were in agreement with the genetic data (isozyme). The method used in this study was polyacrilamide gel electrophoresis using peroxides, estarase, malate dehydrogenase, and acid phosphatase enzymes. The results showed that cluster analysis based on isozyme data have given a good support to classification of eleven species based on morphological groups. This study concluded that in certain species each morphological variation was profit to be genetically based. Key Words: Ranunculus, isozym

RASS-SDSS Galaxy Cluster Survey. VI. The dependence of the cluster SFR on the cluster global properties

Using a subsample of 79 nearby clusters from the RASS-SDSS galaxy cluster catalogue of Popesso et al. (2005a), we perform a regression analysis between the cluster integrated star formation rate (Sigma_SFR) the cluster total stellar mass (M_star), the fractions of star forming (f_SF) and blue (f_b) galaxies and other cluster global properties, namely its richness (N_gal, i.e. the total number of cluster members within the cluster virial radius), velocity dispersion (sigma_v), virial mass (M_200), and X-ray luminosity (L_X). All cluster global quantities are corrected for projection effects before the analysis. Galaxy SFRs and stellar masses are taken from the catalog of Brinchmann et al. (2004), which is based on SDSS spectra. We only consider galaxies with M_r <= -20.25 in our analysis, and exclude AGNs. We find that both Sigma_SFR and M_star are correlated with all the cluster global quantities. A partial correlation analysis show that all the correlations are induced by the fundamental one between Sigma_SFR and N_gal, hence there is no evidence that the cluster properties affect the mean SFR or M_star per galaxy. The relations between Sigma_SFR and M_star, on one side, and both N_gal and M_200, on the other side, are linear, i.e. we see no evidence that different clusters have different SFR or different M_star per galaxy and per unit mass. The fraction f_SF does not depend on any cluster property considered, while f_b does depend on L_X. We note that a significant fraction of star-forming cluster galaxies are red (~25% of the whole cluster galaxy population). We conclude that the global cluster properties are unable to affect the SF properties of cluster galaxies, but the presence of the X-ray luminous intra-cluster medium can affect their colors, perhaps through the ram-pressure stripping mechanism.Comment: 14 pages, 12 figures, accepted for publication on A&A; corrected coefficient in Tab.

Colour cluster analysis for pigment identification

This paper presents image processing algorithms designed to analyse the colour CIE Lab histogram of high resolution images of paintings. Three algorithms are illustrated which attempt to identify colour clusters, cluster shapes due to shading and finally to identify pigments. Using the image collection and pigment list of the National Gallery London large numbers of images within a restricted period have been classified with a variety of algorithms. The image descriptors produced were also used with suitable comparison metrics to obtain content-based retrieval of the images

A robust method for cluster analysis

Let there be given a contaminated list of n R^d-valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with n-r outliers for the parameters of the g populations; it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled determinant criterion of cluster analysis. We also propose an efficient algorithm for approximating this estimator and study its breakdown points for mean values and pooled SSP matrix.Comment: Published at http://dx.doi.org/10.1214/009053604000000940 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Understanding stakeholder values using cluster analysis

The K-Means and Ward’s Clustering procedures were used to categorize value similarities among respondents of a public land management survey. The clustering procedures resulted in two respondent groupings: an anthropocentrically focused group and an ecocentrically focused group. While previous studies have suggested that anthropocentric and ecocentric groups are very different, this study revealed many similarities. Similarities between groups included a strong feeling towards public land and national forest existence as well as the importance of considering both current and future generations when making management decisions for public land. It is recommended that land managers take these similarities into account when making management decisions. It is important to note that using the Ward’s procedure for clustering produced more consistent groupings than the K-Means procedure and is therefore recommended when clustering survey data. K-Means only showed consistency with datasets of over 500 observations